rapidsaiOfficial·8 Agent Skills Included

cudf

GPU-accelerated DataFrame processing, testing, and pull request review

Accelerates pandas, Polars, and Dask DataFrame workloads on NVIDIA GPUs with zero or minimal code changes. Guides AI agents through building, testing, benchmarking, and reviewing cuDF pull requests and CI failures. Eliminates slow CPU data processing, manual build steps, and guesswork when debugging GPU compatibility issues.

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npx skills add rapidsai/cudf --all -g -y

All Skills in This Repository (8)

Pure Emerald Level Indicators

📦 In Repo

rapidsai

build-test-cudf

Build and test code changes inside a cudf devcontainer.

review-cudf

Automates cudf pull request evaluation by collecting metadata, diffs, and context.

review-cudf-polars-expressions

Implements and reviews Polars expression support in the cudf-polars GPU engine.

perf-compare-cudf

Benchmarks libcudf NVBench suites on a branch or PR against main and reports performance differences.

update-narwhals-testing

Updates Narwhals version pins and pytest skip lists for cudf CI testing.

build-test-cudf-java

Build and test cuDF Java bindings with Maven against nightly or local libcudf builds.

debug-cudf-pandas

Diagnose and fix pandas test suite failures under the cudf.pandas GPU compatibility layer.

accelerated-computing-cudf

Migrate pandas DataFrame workloads to GPU-accelerated cuDF and dask-cuDF pipelines.

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Frequently Asked Questions

FAQPage Schema

How to install cuDF skills?▼

Run `npx skills add rapidsai/cudf --all -g -y` in your terminal to install all skills in this suite globally.

How to speed up pandas on a GPU?▼

Load the cudf.pandas extension before importing pandas, and your existing code runs on the GPU with automatic CPU fallback for unsupported operations.

Can AI agents review cuDF pull requests?▼

Yes. The review-cudf skill fetches the PR diff, checks it against official C++ and Python review guidelines, and produces a structured review with findings and a verdict.

How to reproduce cuDF CI failures locally?▼

The reproduce-ci skill parses a GitHub Actions job URL, pulls the same container image, and reruns the exact CI script locally in Docker for debugging.

Do I need a GPU to use these skills?▼

The DataFrame acceleration skills require an NVIDIA GPU with CUDA 12 or 13, while review and CI reproduction skills mainly need Docker and the GitHub CLI.